Managing the performance of complex systems requires more than simply running load tests. You need to perform a careful analysis of test results and production metrics. The sheer amount of data generated makes analysis a challenge that is often left wanting. With machine learning (ML) and the application of data science techniques, you can derive valuable and actionable information from big data. In this episode, Gopal Brugalette, a Sr. Performance Architect/Manager/Software Engineer/ shares the basic concepts behind ML, covering clustering, predictive analysis, and neural networks. He shows you how to implement algorithms using open-source tools and languages like Python, R, and AWS cloud services. With real-world examples, Gopal demonstrates the big data platforms Hadoop, Elasticsearch, and AWS Sagemaker. He illustrates performance engineering problems like performance monitoring, test result comparisons, error message analysis, and user insights. Also, Performance testing is a critical skill that is becoming increasingly important for organizations. Still, unfortunately, it is not a skill many testers possess—having an application that is not performant and not meeting your customer's expectations can be dangerous to your company's reputation and bottom line. Sometimes, it can lead to layoffs as organizations try to cut costs. I believe that anything that can be automated in the SDLC, even if it's not a functional test, should be handled by testers and automation engineers. As a tester, you might think you don't need to know performance testing, but trust me, you do. A tester with multiple testing skills is much more valuable to the company. That is why I'd like to invite you to this year's online Automation Guild. The last two days of the conference (part of the 5-day ticket) include many sessions on performance testing. Register Now: https://guildconferences.com/ag-2023/